Racial Implicit Bias, Treatment Recommendations, and Perceived Compliance in the Care of Juvenile Idiopathic Arthritis.
Academic Article
Overview
abstract
OBJECTIVE: Racial implicit bias may contribute to health disparities in juvenile idiopathic arthritis (JIA) outcomes by impacting provider medical decision-making. Our study assessed racial and racial-medical compliance implicit biases of an international pediatric rheumatology community and investigated whether their biases impact treatment recommendations for patients with JIA. METHODS: A web-based survey, which included a randomized vignette describing either a White or Black patient with JIA, was sent to pediatric rheumatology providers. Participants were prompted to select the best patient management option and to complete two implicit association tests (IATs): race and race compliance. Student's t-tests or analysis of variance were used to compare IAT D-scores between or across groups; all tests were two-sided with P < 0.05 considered statistically significant. Logistic regression models were used to examine associations for two outcomes of interest: recommendation of either adequate (methotrexate monotherapy) or aggressive (methotrexate and adalimumab combination) treatment with each IAT D-score by each vignette. RESULTS: Overall, 165 pediatric rheumatologists completed the survey. Providers showed a slight pro-White bias in the race IAT (mean D-score ± SD 0.26 ± 0.5) and race-medical compliance IAT (mean D-score ± SD 0.16 ± 0.43). Although not statistically significant, a one-point increase in IAT D-scores was associated with a lower likelihood that providers would choose aggressive treatment versus adequate treatment in the Black vignette (odds ratio [OR] 0.55, 95% confidence interval [CI] 0.20-1.47; P = 0.23), and a greater likelihood that providers would choose aggressive treatment in the White vignette (OR 4.07, 95% CI 0.74-22.24; P = 0.11). CONCLUSION: Implicit bias was not associated with treatment recommendations. Further studies are needed to better evaluate the impact of implicit bias.
